Overview

Dataset statistics

Number of variables14
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory84.1 KiB
Average record size in memory112.2 B

Variable types

Numeric8
Boolean3
Categorical3

Alerts

Pregnancies is highly overall correlated with AgeHigh correlation
Age is highly overall correlated with PregnanciesHigh correlation
SkinThickness is highly overall correlated with InsulinHigh correlation
Insulin is highly overall correlated with SkinThicknessHigh correlation
Pregnancies has 111 (14.5%) zerosZeros
BloodPressure has 35 (4.6%) zerosZeros
SkinThickness has 227 (29.6%) zerosZeros
Insulin has 374 (48.7%) zerosZeros
BMI has 11 (1.4%) zerosZeros

Reproduction

Analysis started2023-04-22 07:55:59.018976
Analysis finished2023-04-22 07:56:06.863960
Duration7.84 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Pregnancies
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8450521
Minimum0
Maximum17
Zeros111
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-04-22T13:26:06.931532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3695781
Coefficient of variation (CV)0.87634133
Kurtosis0.15921978
Mean3.8450521
Median Absolute Deviation (MAD)2
Skewness0.90167398
Sum2953
Variance11.354056
MonotonicityNot monotonic
2023-04-22T13:26:07.048759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 135
17.6%
0 111
14.5%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
Other values (7) 58
7.6%
ValueCountFrequency (%)
0 111
14.5%
1 135
17.6%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
ValueCountFrequency (%)
17 1
 
0.1%
15 1
 
0.1%
14 2
 
0.3%
13 10
 
1.3%
12 9
 
1.2%
11 11
 
1.4%
10 24
3.1%
9 28
3.6%
8 38
4.9%
7 45
5.9%

Age
Real number (ℝ)

Distinct52
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.240885
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-04-22T13:26:07.177420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.760232
Coefficient of variation (CV)0.35378816
Kurtosis0.64315889
Mean33.240885
Median Absolute Deviation (MAD)7
Skewness1.1295967
Sum25529
Variance138.30305
MonotonicityNot monotonic
2023-04-22T13:26:07.331559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 72
 
9.4%
21 63
 
8.2%
25 48
 
6.2%
24 46
 
6.0%
23 38
 
4.9%
28 35
 
4.6%
26 33
 
4.3%
27 32
 
4.2%
29 29
 
3.8%
31 24
 
3.1%
Other values (42) 348
45.3%
ValueCountFrequency (%)
21 63
8.2%
22 72
9.4%
23 38
4.9%
24 46
6.0%
25 48
6.2%
26 33
4.3%
27 32
4.2%
28 35
4.6%
29 29
3.8%
30 21
 
2.7%
ValueCountFrequency (%)
81 1
 
0.1%
72 1
 
0.1%
70 1
 
0.1%
69 2
0.3%
68 1
 
0.1%
67 3
0.4%
66 4
0.5%
65 3
0.4%
64 1
 
0.1%
63 4
0.5%

Glucose
Real number (ℝ)

Distinct136
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.89453
Minimum0
Maximum199
Zeros5
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-04-22T13:26:07.485256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79
Q199
median117
Q3140.25
95-th percentile181
Maximum199
Range199
Interquartile range (IQR)41.25

Descriptive statistics

Standard deviation31.972618
Coefficient of variation (CV)0.26446703
Kurtosis0.64077982
Mean120.89453
Median Absolute Deviation (MAD)20
Skewness0.1737535
Sum92847
Variance1022.2483
MonotonicityNot monotonic
2023-04-22T13:26:07.647864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 17
 
2.2%
100 17
 
2.2%
111 14
 
1.8%
129 14
 
1.8%
125 14
 
1.8%
106 14
 
1.8%
112 13
 
1.7%
108 13
 
1.7%
95 13
 
1.7%
105 13
 
1.7%
Other values (126) 626
81.5%
ValueCountFrequency (%)
0 5
0.7%
44 1
 
0.1%
56 1
 
0.1%
57 2
 
0.3%
61 1
 
0.1%
62 1
 
0.1%
65 1
 
0.1%
67 1
 
0.1%
68 3
0.4%
71 4
0.5%
ValueCountFrequency (%)
199 1
 
0.1%
198 1
 
0.1%
197 4
0.5%
196 3
0.4%
195 2
0.3%
194 3
0.4%
193 2
0.3%
191 1
 
0.1%
190 1
 
0.1%
189 4
0.5%

BloodPressure
Real number (ℝ)

Distinct47
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.105469
Minimum0
Maximum122
Zeros35
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-04-22T13:26:07.825611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.7
Q162
median72
Q380
95-th percentile90
Maximum122
Range122
Interquartile range (IQR)18

Descriptive statistics

Standard deviation19.355807
Coefficient of variation (CV)0.28009082
Kurtosis5.1801566
Mean69.105469
Median Absolute Deviation (MAD)8
Skewness-1.843608
Sum53073
Variance374.64727
MonotonicityNot monotonic
2023-04-22T13:26:07.999265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
70 57
 
7.4%
74 52
 
6.8%
78 45
 
5.9%
68 45
 
5.9%
72 44
 
5.7%
64 43
 
5.6%
80 40
 
5.2%
76 39
 
5.1%
60 37
 
4.8%
0 35
 
4.6%
Other values (37) 331
43.1%
ValueCountFrequency (%)
0 35
4.6%
24 1
 
0.1%
30 2
 
0.3%
38 1
 
0.1%
40 1
 
0.1%
44 4
 
0.5%
46 2
 
0.3%
48 5
 
0.7%
50 13
 
1.7%
52 11
 
1.4%
ValueCountFrequency (%)
122 1
 
0.1%
114 1
 
0.1%
110 3
0.4%
108 2
0.3%
106 3
0.4%
104 2
0.3%
102 1
 
0.1%
100 3
0.4%
98 3
0.4%
96 4
0.5%

SkinThickness
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.536458
Minimum0
Maximum99
Zeros227
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-04-22T13:26:08.167321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q332
95-th percentile44
Maximum99
Range99
Interquartile range (IQR)32

Descriptive statistics

Standard deviation15.952218
Coefficient of variation (CV)0.77677549
Kurtosis-0.52007187
Mean20.536458
Median Absolute Deviation (MAD)12
Skewness0.1093725
Sum15772
Variance254.47325
MonotonicityNot monotonic
2023-04-22T13:26:08.403749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 227
29.6%
32 31
 
4.0%
30 27
 
3.5%
27 23
 
3.0%
23 22
 
2.9%
33 20
 
2.6%
28 20
 
2.6%
18 20
 
2.6%
31 19
 
2.5%
19 18
 
2.3%
Other values (41) 341
44.4%
ValueCountFrequency (%)
0 227
29.6%
7 2
 
0.3%
8 2
 
0.3%
10 5
 
0.7%
11 6
 
0.8%
12 7
 
0.9%
13 11
 
1.4%
14 6
 
0.8%
15 14
 
1.8%
16 6
 
0.8%
ValueCountFrequency (%)
99 1
 
0.1%
63 1
 
0.1%
60 1
 
0.1%
56 1
 
0.1%
54 2
0.3%
52 2
0.3%
51 1
 
0.1%
50 3
0.4%
49 3
0.4%
48 4
0.5%

Insulin
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct186
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.799479
Minimum0
Maximum846
Zeros374
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-04-22T13:26:08.525286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30.5
Q3127.25
95-th percentile293
Maximum846
Range846
Interquartile range (IQR)127.25

Descriptive statistics

Standard deviation115.244
Coefficient of variation (CV)1.4441699
Kurtosis7.2142596
Mean79.799479
Median Absolute Deviation (MAD)30.5
Skewness2.2722509
Sum61286
Variance13281.18
MonotonicityNot monotonic
2023-04-22T13:26:08.617129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 374
48.7%
105 11
 
1.4%
130 9
 
1.2%
140 9
 
1.2%
120 8
 
1.0%
94 7
 
0.9%
180 7
 
0.9%
100 7
 
0.9%
135 6
 
0.8%
115 6
 
0.8%
Other values (176) 324
42.2%
ValueCountFrequency (%)
0 374
48.7%
14 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
18 2
 
0.3%
22 1
 
0.1%
23 2
 
0.3%
25 1
 
0.1%
29 1
 
0.1%
32 1
 
0.1%
ValueCountFrequency (%)
846 1
0.1%
744 1
0.1%
680 1
0.1%
600 1
0.1%
579 1
0.1%
545 1
0.1%
543 1
0.1%
540 1
0.1%
510 1
0.1%
495 2
0.3%

BMI
Real number (ℝ)

Distinct248
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.992578
Minimum0
Maximum67.1
Zeros11
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-04-22T13:26:08.711320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.8
Q127.3
median32
Q336.6
95-th percentile44.395
Maximum67.1
Range67.1
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation7.8841603
Coefficient of variation (CV)0.24643717
Kurtosis3.2904429
Mean31.992578
Median Absolute Deviation (MAD)4.6
Skewness-0.42898159
Sum24570.3
Variance62.159984
MonotonicityNot monotonic
2023-04-22T13:26:08.805022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 13
 
1.7%
31.6 12
 
1.6%
31.2 12
 
1.6%
0 11
 
1.4%
32.4 10
 
1.3%
33.3 10
 
1.3%
30.1 9
 
1.2%
32.8 9
 
1.2%
32.9 9
 
1.2%
30.8 9
 
1.2%
Other values (238) 664
86.5%
ValueCountFrequency (%)
0 11
1.4%
18.2 3
 
0.4%
18.4 1
 
0.1%
19.1 1
 
0.1%
19.3 1
 
0.1%
19.4 1
 
0.1%
19.5 2
 
0.3%
19.6 3
 
0.4%
19.9 1
 
0.1%
20 1
 
0.1%
ValueCountFrequency (%)
67.1 1
0.1%
59.4 1
0.1%
57.3 1
0.1%
55 1
0.1%
53.2 1
0.1%
52.9 1
0.1%
52.3 2
0.3%
50 1
0.1%
49.7 1
0.1%
49.6 1
0.1%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4718763
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-04-22T13:26:08.914056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum2.42
Range2.342
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.3313286
Coefficient of variation (CV)0.70215138
Kurtosis5.5949535
Mean0.4718763
Median Absolute Deviation (MAD)0.1675
Skewness1.9199111
Sum362.401
Variance0.10977864
MonotonicityNot monotonic
2023-04-22T13:26:09.040699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.258 6
 
0.8%
0.254 6
 
0.8%
0.268 5
 
0.7%
0.207 5
 
0.7%
0.261 5
 
0.7%
0.259 5
 
0.7%
0.238 5
 
0.7%
0.19 4
 
0.5%
0.263 4
 
0.5%
0.299 4
 
0.5%
Other values (507) 719
93.6%
ValueCountFrequency (%)
0.078 1
0.1%
0.084 1
0.1%
0.085 2
0.3%
0.088 2
0.3%
0.089 1
0.1%
0.092 1
0.1%
0.096 1
0.1%
0.1 1
0.1%
0.101 1
0.1%
0.102 1
0.1%
ValueCountFrequency (%)
2.42 1
0.1%
2.329 1
0.1%
2.288 1
0.1%
2.137 1
0.1%
1.893 1
0.1%
1.781 1
0.1%
1.731 1
0.1%
1.699 1
0.1%
1.698 1
0.1%
1.6 1
0.1%

Polyphagia
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size896.0 B
False
436 
True
332 
ValueCountFrequency (%)
False 436
56.8%
True 332
43.2%
2023-04-22T13:26:09.149644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size896.0 B
False
434 
True
334 
ValueCountFrequency (%)
False 434
56.5%
True 334
43.5%
2023-04-22T13:26:09.223399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Obesity
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size896.0 B
False
638 
True
130 
ValueCountFrequency (%)
False 638
83.1%
True 130
 
16.9%
2023-04-22T13:26:09.298660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Smoker
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
409 
1
359 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 409
53.3%
1 359
46.7%

Length

2023-04-22T13:26:09.362059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-22T13:26:09.439412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 409
53.3%
1 359
46.7%

Most occurring characters

ValueCountFrequency (%)
0 409
53.3%
1 359
46.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 768
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 409
53.3%
1 359
46.7%

Most occurring scripts

ValueCountFrequency (%)
Common 768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 409
53.3%
1 359
46.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 409
53.3%
1 359
46.7%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
1
406 
0
362 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 406
52.9%
0 362
47.1%

Length

2023-04-22T13:26:09.514210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-22T13:26:09.592927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 406
52.9%
0 362
47.1%

Most occurring characters

ValueCountFrequency (%)
1 406
52.9%
0 362
47.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 768
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 406
52.9%
0 362
47.1%

Most occurring scripts

ValueCountFrequency (%)
Common 768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 406
52.9%
0 362
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 406
52.9%
0 362
47.1%

Diabetes Outcome
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
500 
1
268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Length

2023-04-22T13:26:09.655234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-22T13:26:09.730193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring characters

ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 768
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring scripts

ValueCountFrequency (%)
Common 768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Interactions

2023-04-22T13:26:05.762573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:00.547049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:01.308706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:02.118202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:02.868805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:03.588143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:04.282390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:04.962662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:05.863432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:00.652512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:01.397040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:02.217789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:02.972428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:03.674562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:04.376202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:05.061477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:05.956736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:00.750615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:01.487259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:02.305463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:03.059408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:03.761041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:04.463589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:05.149622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:06.054917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:00.850554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:01.639657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:02.403378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:03.154702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:03.854521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:04.560976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:05.241917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:06.150711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:00.935038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:01.731469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:02.499890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:03.242273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:03.940742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:04.640573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:05.413765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:06.234432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:01.037454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:01.812856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:02.591285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:03.330102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:04.028332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:04.714325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:05.502484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:06.313389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:01.117370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:01.894068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:02.677197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:03.407544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:04.106219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:04.795151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:05.585725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:06.408250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:01.210298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:01.995509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:02.776733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:03.502629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:04.192557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:04.884120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-22T13:26:05.671464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-22T13:26:09.795195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
PregnanciesAgeGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionPolyphagiaVisual BlurringObesitySmokerHigh Cholestrol (HDL)Diabetes Outcome
Pregnancies1.0000.6070.1310.185-0.085-0.1270.000-0.0430.0770.1130.0000.0960.0880.235
Age0.6071.0000.2850.351-0.067-0.1140.1310.0430.0750.0270.0000.0510.0000.314
Glucose0.1310.2851.0000.2350.0600.2130.2310.0910.0000.0000.0090.0000.0000.487
BloodPressure0.1850.3510.2351.0000.126-0.0070.2930.0300.0000.0520.0000.0550.0910.152
SkinThickness-0.085-0.0670.0600.1261.0000.5410.4440.1800.0770.0000.0000.0600.0000.208
Insulin-0.127-0.1140.213-0.0070.5411.0000.1930.2210.0000.0000.0790.0000.0000.159
BMI0.0000.1310.2310.2930.4440.1931.0000.1410.0000.0510.0000.0000.0500.317
DiabetesPedigreeFunction-0.0430.0430.0910.0300.1800.2210.1411.0000.0000.0390.0000.0000.0420.173
Polyphagia0.0770.0750.0000.0000.0770.0000.0000.0001.0000.2580.0000.0000.0000.039
Visual Blurring0.1130.0270.0000.0520.0000.0000.0510.0390.2581.0000.0760.0410.0000.000
Obesity0.0000.0000.0090.0000.0000.0790.0000.0000.0000.0761.0000.0000.0000.000
Smoker0.0960.0510.0000.0550.0600.0000.0000.0000.0000.0410.0001.0000.1130.027
High Cholestrol (HDL)0.0880.0000.0000.0910.0000.0000.0500.0420.0000.0000.0000.1131.0000.000
Diabetes Outcome0.2350.3140.4870.1520.2080.1590.3170.1730.0390.0000.0000.0270.0001.000

Missing values

2023-04-22T13:26:06.567312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-22T13:26:06.762574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PregnanciesAgeGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionPolyphagiaVisual BlurringObesitySmokerHigh Cholestrol (HDL)Diabetes Outcome
06501487235033.60.627NoNoYes111
1131856629026.60.351NoYesNo100
2832183640023.30.672YesNoNo011
31218966239428.10.167YesNoNo000
4033137403516843.12.288YesYesYes011
5530116740025.60.201YesYesYes110
63267850328831.00.248YesNoNo101
7102911500035.30.134NoYesNo110
8253197704554330.50.158YesNoYes111
985412596000.00.232YesYesNo001
PregnanciesAgeGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionPolyphagiaVisual BlurringObesitySmokerHigh Cholestrol (HDL)Diabetes Outcome
758126106760037.50.197YesYesNo110
759666190920035.50.278YesYesNo001
7602228858261628.40.766NoNoNo010
7619431707431044.00.403NoNoNo001
76293389620022.50.142NoYesNo110
7631063101764818032.90.171NoNoNo100
7642271227027036.80.340NoYesNo010
765530121722311226.20.245NoNoNo000
766147126600030.10.349NoYesNo011
767123937031030.40.315NoYesYes010